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Lookup NU author(s): Dr Gerard HallORCiD, Frances Hutchings, Jonathan Horsley, Callum Simpson, Professor Yujiang WangORCiD, Professor Peter TaylorORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 Massachusetts Institute of Technology.Extra temporal lobe epilepsy (eTLE) may involve heterogenous widespread cerebral networks. We investigated the structural network of an eTLE cohort, at the postulated epileptogenic zone later surgically removed, as a network node: the resection zone (RZ). We hypothesized patients with an abnormal connection to/from the RZ to have proportionally increased abnormalities based on topological proximity to the RZ, in addition to poorer post-operative seizure outcome. Structural and diffusion MRI were collected for 22 eTLE patients pre-and post-surgery, and for 29 healthy controls. The structural connectivity of the RZ prior to surgery, measured via generalized fractional anisotropy (gFA), was compared with healthy controls. Abnormal connections were identified as those with substantially reduced gFA (z < −1.96). For patients with one or more abnormal connections to/from the RZ, connections with closer topological distance to the RZ had higher proportion of abnormalities. The minority of the seizure-free patients (3/11) had one or more abnormal connections, while most non-seizure-free patients (8/11) had abnormal connections to the RZ. Our data suggest that eTLE patients with one or more abnormal structural connections to/from the RZ had more proportional abnormal connections based on topological distance to the RZ and associated with reduced chance of seizure freedom post-surgery.
Author(s): Hall GR, Hutchings F, Horsley J, Simpson CM, Wang Y, de Tisi J, Miserocchi A, McEvoy AW, Vos SB, Winston GP, Duncan JS, Taylor PN
Publication type: Article
Publication status: Published
Journal: Network Neuroscience
Year: 2023
Volume: 7
Issue: 4
Pages: 1351-1362
Online publication date: 22/12/2023
Acceptance date: 22/06/2023
Date deposited: 18/12/2023
ISSN (electronic): 2472-1751
Publisher: MIT Press Journals
URL: https://doi.org/10.1162/netn_a_00327
DOI: 10.1162/netn_a_00327
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